
TradeSense
Detect behavioral biases hiding in your trading history
What it does
Most traders track returns. Nobody tracks behavior. TradeSense analyzes your broker's CSV export using 3 ML models and detects if you're a panic seller, overtrades, or short-term thinker patterns that silently kill your returns. Upload your CSV → get a behavioral risk score + full bias breakdown in minutes. Free, no login needed. Works with Zerodha, Groww, or any broker CSV.
Does the same job
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- IBI built a tool to find trading signals that aren't just random luckSep 2025 · hikaro.app · ▲9
Hi HN, I'm a solo dev and for the last few months I've been building Hikaro, a tool to find statistically significant trading signals for [e.g., US equities, crypto, forex]. I built this to solve my own problem: I was tired of backtests that looked great on paper but failed in practice. Simple metrics like "win rate" can be misleading, so I wanted a way to quickly tell if a signal's performance was genuine or just noise. Hikaro ingests daily market data and runs statistical analysis on various trading signals. The goal is to surface signals with strong properties, like: Low p-value: Evidence…
TradeAnalysisProApr 2026 · tradeanalysispro.com · ▲1Track your trades. Understand your behavior. Improve faster
TradeWiseJun 2026 · mytradewiseoc.com · ▲6AI trading journal that tells you why you're losing trades
- TATradeSight – a Rust-powered market risk dashboard with AI analysis2024 · tradesight.live · ▲5
I built TradeSight (https://tradesight.live) as a lightweight market risk indicator that combines real-time data with AI insights. The backend is written in Rust, with a vanilla JS frontend for maximum performance. It aggregates data from multiple sources (FRED API, Yahoo Finance) and uses Claude's API to provide detailed market analysis. Technical stack: - No login required - static page with hourly updates - Rust backend for efficient data aggregation - Vanilla JavaScript frontend for minimal overhead - Claude API integration for real-time market analysis - Data sources: FRED…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, March 2026
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Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com


